Gene function correlates with potential for G4 DNA formation in the hu
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《核酸研究医学期刊》
1 Molecular and Cellular Biology Graduate Program, University of Washington School of Medicine 1959 NE Pacific Street, Seattle, WA 98195-7650, USA 2 Department of Immunology, University of Washington School of Medicine 1959 NE Pacific Street, Seattle, WA 98195-7650, USA 3 Department of Biochemistry, University of Washington School of Medicine 1959 NE Pacific Street, Seattle, WA 98195-7650, USA
*To whom correspondence should be addressed. Tel: +1 206 221 6876; Fax: +1 206 221 6781; Email: maizels@u.washington.edu
ABSTRACT
G-rich genomic regions can form G4 DNA upon transcription or replication. We have quantified the potential for G4 DNA formation (G4P) of the 16 654 genes in the human RefSeq database, and then correlated gene function with G4P. We have found that very low and very high G4P correlates with specific functional classes of genes. Notably, tumor suppressor genes have very low G4P and proto-oncogenes have very high G4P. G4P of these genes is evenly distributed between exons and introns, and it does not reflect enrichment for CpG islands or local chromosomal environment. These results show that genomic structure undergoes selection based on gene function. Selection based on G4P could promote genomic stability (or instability) of specific classes of genes; or reflect mechanisms for global regulation of gene expression.
INTRODUCTION
Eukaryotic genomes contain characteristically G-rich regions, including single-copy genes; the rDNA; and repetitive sequences, such as the telomeres and the immunoglobulin heavy chain switch (S) regions of higher vertebrates. G-rich nucleic acids have the potential to form G-quadruplex or ‘G4 DNA’, a structure in which intra- or inter-strand interactions are stabilized by G-quartets, planar arrays of four guanines, paired by Hoogsteen bonding (1,2). G-quartets can stabilize a remarkable diversity of structures, in which the lengths and positions of the G-runs and the ‘loops’ separating them both contribute to overall topology (3,4). In the human genome, the number of distinct sites with potential to form G4 DNA is estimated at more than 300 000, and specific loop sequences are prominent at some of these sites (5,6).
Key cellular processes are identified with repetitive G-rich chromosomal regions, where regulated formation of G4 DNA may contribute to biological function. At the G-rich telomere tails, the presence of G4 DNA inhibits extension by telomerase, and proteins that bind specifically to telomeric sequences regulate the formation and resolution of G4 DNA (7–15). The G-rich immunoglobulin switch regions are sites of recombination that is critical to B-cell development and the immune response, and regulated transcription of the switch regions induces the formation of DNA structures targeted by factors essential to class switch recombination (16,17).
G-rich regions can also be sites of unprogrammed genomic instability. Many B-cell lymphomas carry a translocation of the MYC proto-oncogene to the immunoglobulin heavy chain switch region (18), and the common translocation breakpoints map to G-rich regions of MYC that form structures similar to those formed by transcribed G-rich switch regions (19,20). Some of the most unstable human minisatellites are G-rich sequences predicted to form G4 DNA (21); and G4 DNA formation in vitro has been directly confirmed for two G-rich VNTRs, D4S43, and the insulin-linked hypervariable repeat (22). Reporter constructs carrying interstitial telomeric repeats display high levels of instability (23), which may be analogous to the instability of G-rich VNTRs.
Specialized mechanisms may regulate the expression of G-rich genes at the levels of transcription, RNA processing and translation. Cotranscriptional RNA:DNA hybrid formation occurs readily within G-rich regions (19,24,25). Factors associated with RNA processing pathways, including THO/TREX and ASF/SF2, normally prevent cotranscriptional RNA:DNA hybrid formation, and promote gene expression; and genomic instability ensues in their absence (26,27). Factors involved in translational regulation may target RNA transcripts that contain G-quartets (28,29). Regions with the potential to form G4 DNA have been identified in the promoters of several proto-oncogenes, including c-MYC, VEGF, c-KIT and BCL2 (30–33). This has led to suggestions that formation or resolution of specific quadruplex structures may contribute to the regulation of gene expression, and prompted the design of therapeutics targeted to these structures, but the biological specificity of such compounds is yet to be established rigorously (34–39).
Conserved and ubiquitous repair factors recognize G4 DNA, including the human RecQ family helicases BLM and WRN (40,41); the Saccharomyces cerevisiae RecQ family helicase Sgs1 (42); and the mismatch repair factor MutS, a heterodimer of MSH2/MSH6 (16). RecQ family helicases maintain G-rich regions during replication. Sgs1 is required for nucleolar stability and replication of the G-rich rDNA (43,44); and in the absence of WRN helicase, telomeric sequence is lost due to impaired replication of the G-rich strand (45). The mismatch repair factor, MutS, may cooperate with BLM helicase to promote the resolution of G4 DNA during replication (46). In immunoglobulin switch recombination, MutS recognizes G4 DNA formed during transcription of the G-rich switch regions to promote their synapsis and recombination (16).
Genomic regions with potential to form G4 DNA have been enumerated (4,5), but they have not been correlated with specific gene functions. The link between potential for G4 DNA formation and genomic instability suggests that the identification of human genes with relatively high or low potential to form G4 DNA might provide insights into the evolution of genomic structure, or identify mechanisms that could account for genomic instability in human malignancies. The possibility that G-richness can contribute to shared regulation suggests that genes with similar or related functions may share features of genomic structure. We therefore set out to determine the prevalence of G-rich sequences capable of forming G4 DNA among human genes, and to determine if particular functional classes of genes might be characterized by the presence or absence of G-rich regions.
METHODS
G4P Calculator software
We developed a software program, ‘G4P Calculator’, which computes G4 DNA potential based on the density of runs of guanines in a sequence. The program evaluates runs of guanines in a sliding window and calculates the percentage of windows searched that meet the specified criteria. The criteria used are as follows: G-run length, 3; number of G-runs per window, 4; window length, 100 nt; and sliding interval length, 20 nt. The requirement for four or more runs of three or more guanines is based on studies of oligonucleotide folding . The 100 nt window size facilitates rapid analysis and is easily reduced (or enlarged) for rescan of specific genes of interest. The 20 nt sliding interval length is set such that sequences with the potential to form more than a single G4 DNA structure make correspondingly higher contributions to the total G4P. The last four windows analyzed are processed as windows of progressively smaller size (80, 60, 40 and 20 nt), and this does not affect the results as gene length is much larger than window size. Each DNA strand is evaluated independently. G4P is scored as a percentage, making it independent of sequence length. These criteria are similar to those used by others (4,5), and although not absolute, provide a means to compare G4P between different sequences. The software is written in C# to run on the Microsoft Windows XP operating system. The program and instructions are available on our laboratory website (http://depts.washington.edu/maizels9/). The source code is available upon request.
Sequence and GO data
Sequence data for the human RefSeq genes (NCBI 35 assembly) and associated GO terms were downloaded from the Ensembl database v.32 using BioMart (47) on 4/9/05. Additional Gene Ontology (GO) data were obtained from the GO website (www.geneontology.org) on 3/5/06. Flanking sequence data were downloaded from Ensembl v.34 on 14/10/05. The cDNA sequences were downloaded from the Ensembl v.37 on 19/3/06. For genes with multiple transcript variants, the first listed variant for each gene was evaluated to assess G4P of the cDNA. The median G4 DNA potential was calculated from 16 654 RefSeq genes. Since each gene may have several GO classifications, a total of 77 968 GO term assignments were sorted above or below the median corresponding to 50.5% or 49.5% of the total. The 4524 RefSeq genes that have no GO classification (27% of the total) are equally distributed below and above the median.
Flanking sequence analysis
Analysis of G4P included only genes for which both gene and flanking sequence were complete. Excluded from that analysis were 101 genes for which sequence determination was incomplete (more than four unidentified consecutive bases). The excluded genes are identified in Supplementary Table 1; none of the excluded genes was a known proto-oncogene or a tumor suppressor gene.
Statistical analysis
The Wilcoxon rank sum test was applied by using the statistics program R 2.2.1 (Wilcoxon test parameters: alternative = ‘two-sided’, paired = FALSE). The linear regression, single-factor ANOVA, and standard error analyses were performed using Microsoft Office Excel 2003. Owing to the skewed distribution of G4P, data were subjected to a natural log transformation before linear regression analysis. Genes with G4P = 0 were therefore not included. This resulted in the exclusion of 1152 genes from the correlation between DNA strands (Figure 1B); and of 905 genes from the analysis of G4P versus GC content (Figure 3A).
Figure 1 Potential for G4 DNA formation of human genes. (A) Distribution of genes across G4 DNA formation potential (G4P). The distribution of 16 654 RefSeq genes is illustrated by vertical bars (gray). Median G4P for the RefSeq genes at 5.0% is indicated by a dotted line. The distribution of the 4396 GO terms assigned to 73% of the RefSeq genes is outlined (black). (B) Positive correlation of G4P of template and nontemplate DNA strands. Linear regression analysis of G4P of the nontemplate (y-axis) and the template (x-axis) strand. Owing to the skewed distribution of G4P, the data were subjected to a natural log transformation before linear regression analysis; therefore, a small number of genes with G4P equal to zero were not included. The slope determined by linear regression analysis (0.83) is represented by the solid line; and slope of unity by the dotted line.
CpG islands
The NewCpGReport software (48) was accessed and run from the website http://csc-fserve.hh.med.ic.ac.uk/emboss/newcpgreport.html. The tumor suppressor and proto-oncogene sequences were processed using the program's default settings: window size = 100; shift increment = 1; minimum length = 200; minimum observed/expected = 0.6; and minimum percent = 50.
RESULTS
G4 DNA formation potential of human genes
To score potential for G4 DNA formation, we developed software that analyzes overlapping windows of sequence, and scores each window that contains four or more runs of three or more guanines as a ‘hit’, then quantifies G4 DNA formation potential (G4P) as the percentage of hits in the total number of windows searched. We used this program, ‘G4P Calculator’, to evaluate the G4P of the entire transcribed sequence (exons and introns) of 16 654 human Reference Sequence (RefSeq) genes. Nontemplate and template strands were analyzed separately, to distinguish contributions of transcription-induced structure formation, which affects only the nontemplate strand; and replication-induced structure formation, which affects both strands. G4P of the nontemplate strand ranged from 0 to 79%, with a median of 5.0% and an average of 9.1% (Figure 1A and Supplementary Table 1). Linear regression analysis (Figure 1B) showed that G4P of the nontemplate and template strands is positively correlated (R2 = 0.73), with a slope of less than unity (0.83). Thus, for most genes, there is a slightly lower potential for the formation of transcription-induced structures than replication-induced structures; although potential for the formation of structures on either strand is closely correlated.
Gene function corresponds with G4P
The skewed distribution of G4P over the RefSeq genes (Figure 1A) suggests that most genes cannot readily form G4 DNA structures, but that some genes may be highly susceptible. To identify functional classes of genes with high and low potential for G4 DNA formation, we evaluated the distribution of terms defined by the GO Consortium (49) for each RefSeq gene across the spectrum of G4 DNA potentials. In this classification scheme, 27% of genes currently have no GO terms assigned; and others have been assigned multiple GO terms and are represented in several different categories. The distribution of the 4396 GO terms assigned to the human RefSeq genes across G4 DNA potential proved to be nearly identical to the distribution of genes (Figure 1A).
Restricting further analysis to the 218 GO terms associated with 50 or more genes, 61 GO terms were identified for which 60% or more of the genes were below or above the RefSeq median G4P (24 low G4P and 37 high G4P); and application of the Wilcoxon rank sum test confirmed that these criteria were robust (Table 1 and Supplementary Table 2). Functions characterized by low G4P include G-protein-coupled receptors, sensory perception (especially olfaction), nucleosome assembly, nucleic acid binding, ubiquitin cycle, cell adhesion and cell division; whereas functions characterized by high G4P include transcription factor activity, development, cell signaling, muscle contraction, growth factors and cytokines. Figure 2 represents, for a subset of the GO terms identified with very low and very high G4P, the median and range of G4P relative to all RefSeq genes and all GO terms. In each case, the difference in distribution relative to the RefSeq genes was highly significant (Figure 2). This establishes a relationship between specific gene functions and potential for G4 DNA formation.
Table 1 Gene Ontology (GO) terms with low and high G4P
Figure 2 G4P correlates with gene function. Ranges of G4P for all RefSeq genes (top line) compared with five GO terms overrepresented in low or high G4P. Boxes represent the percentage of genes in each GO category characterized by G4P in the range 0–1.25, 1.25–2.5, 2.5–5.0, 5–10, 10–20% and >20% (colors as indicated). P-values shown on the right represent significance of the difference in distribution between each GO term and the RefSeq genes, as calculated by the Wilcoxon rank sum test.
Tumor suppressor genes are characterized by low G4P and proto-oncogenes by high G4P
Some of the gene functions characterized by low and high G4P (Figure 2 and Table 1) are associated with tumor suppressor genes and proto-oncogenes, respectively. This led us to interrogate the distribution of G4P with respect to genes in these two categories. A list of 55 tumor suppressor genes and 95 proto-oncogenes was compiled (Supplementary Table 3), using the Online Mendelian Inheritance in Man (OMIM) database as a primary source and confirming gene classification by search of the published literature. Comparison of G4P for tumor suppressor genes and proto-oncogenes established a clear and highly significant difference in the range of G4P observed (Wilcoxon rank sum test, P = 10–8; Figure 3A), and in the distribution of G4P for genes in these two categories relative to the 16 654 genes in the RefSeq database (Figure 3B). The distribution of tumor suppressor genes was shifted from the RefSeq median of 5.0% towards low G4 DNA potential with a median of 2.4% (Wilcoxon rank sum test, P = 4 x 10–5); and the distribution of proto-oncogenes was shifted towards high G4P with a median of 11.0% (Wilcoxon rank sum test, P = 7 x 10–5).
Figure 3 Contrasting G4P of tumor suppressor genes and proto-oncogenes. (A) Ranges of G4P for 55 tumor suppressor genes, 95 proto-oncogenes and all 16 654 RefSeq genes. Boxes represent the percentage of genes in each category characterized by G4P in the range 0–1.25, 1.25–2.5, 2.5–5.0, 5–10, 10–20% and >20% (colors as indicated). P-value represents significance of the difference in distribution between the tumor suppressor genes and proto-oncogenes, as calculated by the Wilcoxon rank sum test. (B) Distribution of tumor suppressor genes and proto-oncogenes across G4P. Bars represent the G4P distribution of 55 tumor suppressor genes (blue) and 95 proto-oncogenes (red). The black outline diagrams distribution of all 16 654 RefSeq genes (as in Figure 1A). P-values represent significance of the difference in distribution between each group of genes and the RefSeq genes, as calculated by the Wilcoxon rank sum test.
Table 2 shows the top 10 genes in each category, ranked according to G4P, using a high stringency 40 nt search window. Analysis of G4P using a 40 nt rather than a 100 nt search window decreased the numerical value of G4P for each individual gene, as expected (Table 2 and Supplementary Table 3), but did not affect the relative differences in distribution of the potentials, and further supported the significance of the difference between tumor suppressor genes and proto-oncogenes (Wilcoxon rank sum test, P = 2 x 10–7). Table 2 also shows representative GO terms assigned to each gene, many of which correspond to GO terms overrepresented in low or high G4P (Figure 2).
Table 2 Tumor suppressor genes with low G4P and proto-oncogenes with high G4P
Simulations were carried out to verify the significance of the differences between G4P of tumor suppressor genes and proto-oncogenes. G4P distributions of 95 or 55 genes picked at random were not significantly different from the RefSeq set (P > 0.1), in each of 20 iterations. Furthermore, statistical significance of observed differences was robust to misclassification of up to 10 genes per category, as tested by the addition of 10 randomly selected RefSeq genes to either category (P < 0.002), or by elimination of 10 randomly selected genes from either category (P < 0.0002). These simulations confirmed the significance of the differences between G4P of tumor suppressor genes and proto-oncogenes.
Tumor suppressor genes and proto-oncogenes have similar numbers of CpG islands
CpG islands are associated with a majority of promoters of human genes (50), and CpG dinucleotides are targets for methylation leading to gene silencing . G4P is positively correlated with GC-content (Figure 4A), which could in principle reflect a local enrichment of CpG methylation sites. We tested this possibility by analyzing tumor suppressor genes and proto-oncogenes with the EMBOSS program ‘NewCpGReport’ (48), which identifies CpG-rich regions. The number of CpG-rich regions do not differ significantly between these two categories of genes (Wilcoxon rank sum test, P = 0.4; Figure 4B). Thus, the density of potential methylation sites does not distinguish tumor suppressor genes from proto-oncogenes, or account for the differences in G4P we have documented.
Figure 4 G4P correlates with GC-content but not CpG islands. (A) Correlation of G4P with GC-content. Linear regression analysis of G4P relative to total GC-content (left); the portion of GC-content contributed from G-runs or C-runs (center); the remaining GC-content contributed from Gs and Cs outside of G-runs or C-runs (right). The data were subjected to a natural log transformation before linear regression analysis; therefore, a small number of genes with G4P equal to zero were not included. The slopes determined by linear regression analysis are represented by solid lines. G4P correlates most closely with Gs and Cs within runs (middle). (B) Distribution of tumor suppressor genes and proto-oncogenes relative to number of CpG islands. Closed bars, tumor suppressor genes; open bars, proto-oncogenes. P-value was determined by the Wilcoxon rank sum test comparing tumor suppressor genes to proto-oncogenes, and shows that there is not a significant relationship between gene function and number of CpG islands.
Both exon and intron sequences contribute to G4P
In human genes, the ratio of exon length to intron length is typically well below unity (52), so the measurement of G4P for an entire gene will largely reflect the contribution of intronic sequences. To distinguish contributions of exons and introns to G4P, we analyzed the G4P of one representative cDNA for each tumor suppressor gene and proto-oncogene (Figure 5 and Supplementary Table 3). The median G4P of cDNA sequences is 1.9% for tumor suppressor genes, and 7.6% for proto-oncogenes (Figure 5), in each case slightly lower than G4P for the entire gene (2.4 and 11%, respectively; Figure 3B). The difference between G4P for cDNAs in the two functional categories is highly significant (Wilcoxon rank sum test, P = 6 x 10–6). Thus, both exon and intron sequences contribute to the significant differences in G4P characteristic of tumor suppressor and proto-oncogenes.
Figure 5 Differences in G4P of tumor suppressor and proto-oncogene cDNAs. Distribution of tumor suppressor gene and proto-oncogene cDNA sequences across G4P. Bars represent tumor suppressor genes (closed bars) and proto-oncogenes (open bars). P-value was determined from the Wilcoxon rank sum test comparing tumor suppressor genes to proto-oncogenes.
The G4P of genes contrasts with their genomic environment
The human genome consists of large segments of fairly homogeneous GC-content, defined as isochores (53). With the availability of the human genome sequence, this definition has been honed further, and 100 kb segments of DNA sequence can be sorted into five isochore families with an average SD of 1% GC (54). Since G4P is positively correlated with GC-content (Figure 4A), we asked if G4P for each gene reflects its local genomic environment. To do this, we computed the difference in G4P for each of the RefSeq genes and its flanking sequences, G4P = G4P – G4PFLANK, calculating G4PFLANK as the average G4P for 20 kb upstream and downstream of each gene (Supplementary Table 1 and Figure 6). The average G4P for all RefSeq genes is 1.6%; thus on average, genes have greater G4P than their flanking sequences. Comparison of G4P of the RefSeq genes, the proto-oncogenes and the tumor suppressor genes by a single-factor ANOVA showed that the three groups are distinct (ANOVA, P = 10–5; Figure 6). The average G4P for the set of tumor suppressor genes is –2.2%, much lower than that of the RefSeq genes (ANOVA, P = 5 x 10–5). In contrast, the average G4P for the set of proto-oncogenes is 3.4%, higher than that of the RefSeq genes (ANOVA, P = 0.01), and considerably higher than that of the tumor suppressor genes (ANOVA, P = 5 x 10–7). Thus, on average, tumor suppressor genes have lower G4P than their flanking sequences, and proto-oncogenes have higher G4P than their flanking sequences. Potential for G4 DNA formation therefore correlates with gene function, rather than local genomic environment, for both tumor suppressor genes and proto-oncogenes.
Figure 6 G4P of genes differs from G4P of genomic environment. Average G4P for genes; 20 kb flanking sequences (G4PFLANK); and G4P, the difference between G4P for each gene and its flank. Gray bars, RefSeq genes; closed bars, tumor suppressor genes; and open bars, proto-oncogenes. Standard errors were determined by ANOVA for each analysis of the three groups of genes.
The prototypical tumor suppressor and proto-oncogenes, TP53 and MYC , illustrate the relationship between G4P of genes and their flanking sequences. For TP53, G4P is 7.6%, slightly higher than the RefSeq median of 5%; and G4PFLANK is 12.1% (10.0% upstream and 14.1% downstream). Thus, G4P of TP53 is –4.4%, low even among tumor suppressor genes. In contrast, for MYC, G4P is 18.6%, well above the RefSeq median; and G4PFLANK is 2.8% (3.3% upstream and 2.2% downstream). Thus, G4P of MYC is 15.9%, considerably above the average RefSeq G4P of 1.6%.
DISCUSSION
We have investigated the relationship between potential to form G4 DNA and gene function for the 16 654 human RefSeq genes. We find that there is a highly skewed distribution of G4P among human genes, and that there are robust correlations between G4P and gene function. Interrogation of the subset of 218 GO terms assigned to 50 or more genes showed that low G4P corresponds with functions including G-protein-coupled receptors, olfaction, nucleosome assembly, nucleic acid binding, ubiquitin cycle, cell adhesion and cell division; and high G4P with functions including transcription factor activity, development, cell signaling, growth factors and cytokines. These findings motivated interrogation of two contrasting gene categories defined by the OMIM database, tumor suppressor genes and proto-oncogenes, which showed that genes in these categories are distinguished by low and high G4P, respectively (Figure 3).
In contrast to the robust relationship between G4P and gene function, G4P did not correspond to any of several well-established parameters used to characterize genomic structure. G4P does correlate with GC-content, but not with the number of CpG islands (Figure 4). Both exons and introns contribute to the difference in G4P between tumor suppressor genes and proto-oncogenes (Figure 5). Furthermore, G4P does not reflect the local genomic environment (Figure 6). In fact, tumor suppressor genes have much lower G4P than would be predicted by their genomic environment as compared to the RefSeq genes, whereas proto-oncogenes have higher G4P than would be predicted. The most straightforward interpretation of these results is that genes with specific functions have undergone selection based on G4P.
One source of selective pressure that could contribute to determining G4P is suggested by the association between G-rich regions and genomic instability. Transcription-induced or replication-induced DNA structures can form within regions of high G4P, and if these structures are not faithfully resolved, the result may be genomic instability and impaired gene function. In this view, the low G4P of tumor suppressor genes could reflect evolution that minimized potential instability of genes which function to maintain genomic stability. There is considerable evidence for haploinsufficiency of tumor suppressor genes , and this would contribute to pressure to minimize genomic instability. Conversely, the high G4P that characterizes the proto-oncogenes would be predicted to contribute to their destabilization. Could instability provide a selective advantage? Under some circumstances, it may. Proto-oncogenes are transcribed in rapidly dividing cells and tissues. Transcription-induced structures have considerable potential to contribute to genomic instability (25,26), but they can form only within genes, which represent a relatively small fraction of genomic DNA. The high G4P of the proto-oncogenes would make them targets for transcription-induced destabilization. Proto-oncogenes encode key factors that promote cell proliferation and development, and impaired expression of a proto-oncogene could in turn diminish or prevent cell proliferation, either by decreasing expression of an essential factor, or signaling cell death via apoptosis. Proto-oncogenes may therefore carry out a passive surveillance function, monitoring instability that specifically affected the transcribed fraction of the genome. This surveillance function would necessarily be vested in genes, rather than in the vast landscape of nontranscribed sequences, consistent with the clear differences between G4P of genes and their flanking sequences.
Another mechanism that may contribute to selection based on G4P is shared regulation. Sequences within promoter regions of several proto-oncogenes have been shown to form G4 DNA in vitro (29–32), and factors that bind G4 DNA have been implicated in both transcriptional and translational regulation (28,57). However, regulatory factors typically exert their effects within limited genomic regions, so commonality of short cis-regulatory elements is unlikely to provide a complete explanation for a feature of sequence composition that distinguishes both exons and introns, and extends throughout a gene (Figure 5). Similarly, G4P is unlikely to reflect selection for coding capacity, as this sort of selection would affect exons alone. Nonetheless, there does appear to be some selection against regions of high G4P within exons, as in both gene categories, the median G4P of exons was lower than for introns: 1.9% versus 2.4% for tumor suppressor genes; and 7.6% versus 11% for proto-oncogenes (Figures 3B and 5). Thus high G4P may be disfavored in mature RNAs, as has been proposed previously (4); or incompatible with efficient translation or effective coding.
Several lines of evidence suggest that GC-content may broadly correlate with gene expression levels (58–60); in particular, GC-richness correlates with open chromatin structure, which may in turn facilitate transcription (61). Proto-oncogenes are rapidly transcribed during early development and in response to cell activation, and the high G4P of the proto-oncogenes might reflect GC-richness that contributes to high transcription levels of genes in this group. The finding that potential for G4 DNA formation correlates robustly with specific gene functions suggests that G4P may be a useful parameter to include in global analyses of gene expression, regulation and interactions. Systems-based analyses of this sort should establish whether regulation could contribute to selection based on G4P.
SUPPLEMENTARY DATA
Supplementary Data are available at NAR Online.
ACKNOWLEDGEMENTS
We thank Audrey Qiuyan Fu and Paul Sampson for assistance with statistical analysis; John Newman for independent testing of the G4P Calculator software; and Evan Eichler for comments on the manuscript. This work was supported by NIH R01 GM65988 to N.M. and J.E. was supported by T32 CA009537 . Funding to pay the Open Access publication charges for this article was provided by NIH RO1 GM65988.
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*To whom correspondence should be addressed. Tel: +1 206 221 6876; Fax: +1 206 221 6781; Email: maizels@u.washington.edu
ABSTRACT
G-rich genomic regions can form G4 DNA upon transcription or replication. We have quantified the potential for G4 DNA formation (G4P) of the 16 654 genes in the human RefSeq database, and then correlated gene function with G4P. We have found that very low and very high G4P correlates with specific functional classes of genes. Notably, tumor suppressor genes have very low G4P and proto-oncogenes have very high G4P. G4P of these genes is evenly distributed between exons and introns, and it does not reflect enrichment for CpG islands or local chromosomal environment. These results show that genomic structure undergoes selection based on gene function. Selection based on G4P could promote genomic stability (or instability) of specific classes of genes; or reflect mechanisms for global regulation of gene expression.
INTRODUCTION
Eukaryotic genomes contain characteristically G-rich regions, including single-copy genes; the rDNA; and repetitive sequences, such as the telomeres and the immunoglobulin heavy chain switch (S) regions of higher vertebrates. G-rich nucleic acids have the potential to form G-quadruplex or ‘G4 DNA’, a structure in which intra- or inter-strand interactions are stabilized by G-quartets, planar arrays of four guanines, paired by Hoogsteen bonding (1,2). G-quartets can stabilize a remarkable diversity of structures, in which the lengths and positions of the G-runs and the ‘loops’ separating them both contribute to overall topology (3,4). In the human genome, the number of distinct sites with potential to form G4 DNA is estimated at more than 300 000, and specific loop sequences are prominent at some of these sites (5,6).
Key cellular processes are identified with repetitive G-rich chromosomal regions, where regulated formation of G4 DNA may contribute to biological function. At the G-rich telomere tails, the presence of G4 DNA inhibits extension by telomerase, and proteins that bind specifically to telomeric sequences regulate the formation and resolution of G4 DNA (7–15). The G-rich immunoglobulin switch regions are sites of recombination that is critical to B-cell development and the immune response, and regulated transcription of the switch regions induces the formation of DNA structures targeted by factors essential to class switch recombination (16,17).
G-rich regions can also be sites of unprogrammed genomic instability. Many B-cell lymphomas carry a translocation of the MYC proto-oncogene to the immunoglobulin heavy chain switch region (18), and the common translocation breakpoints map to G-rich regions of MYC that form structures similar to those formed by transcribed G-rich switch regions (19,20). Some of the most unstable human minisatellites are G-rich sequences predicted to form G4 DNA (21); and G4 DNA formation in vitro has been directly confirmed for two G-rich VNTRs, D4S43, and the insulin-linked hypervariable repeat (22). Reporter constructs carrying interstitial telomeric repeats display high levels of instability (23), which may be analogous to the instability of G-rich VNTRs.
Specialized mechanisms may regulate the expression of G-rich genes at the levels of transcription, RNA processing and translation. Cotranscriptional RNA:DNA hybrid formation occurs readily within G-rich regions (19,24,25). Factors associated with RNA processing pathways, including THO/TREX and ASF/SF2, normally prevent cotranscriptional RNA:DNA hybrid formation, and promote gene expression; and genomic instability ensues in their absence (26,27). Factors involved in translational regulation may target RNA transcripts that contain G-quartets (28,29). Regions with the potential to form G4 DNA have been identified in the promoters of several proto-oncogenes, including c-MYC, VEGF, c-KIT and BCL2 (30–33). This has led to suggestions that formation or resolution of specific quadruplex structures may contribute to the regulation of gene expression, and prompted the design of therapeutics targeted to these structures, but the biological specificity of such compounds is yet to be established rigorously (34–39).
Conserved and ubiquitous repair factors recognize G4 DNA, including the human RecQ family helicases BLM and WRN (40,41); the Saccharomyces cerevisiae RecQ family helicase Sgs1 (42); and the mismatch repair factor MutS, a heterodimer of MSH2/MSH6 (16). RecQ family helicases maintain G-rich regions during replication. Sgs1 is required for nucleolar stability and replication of the G-rich rDNA (43,44); and in the absence of WRN helicase, telomeric sequence is lost due to impaired replication of the G-rich strand (45). The mismatch repair factor, MutS, may cooperate with BLM helicase to promote the resolution of G4 DNA during replication (46). In immunoglobulin switch recombination, MutS recognizes G4 DNA formed during transcription of the G-rich switch regions to promote their synapsis and recombination (16).
Genomic regions with potential to form G4 DNA have been enumerated (4,5), but they have not been correlated with specific gene functions. The link between potential for G4 DNA formation and genomic instability suggests that the identification of human genes with relatively high or low potential to form G4 DNA might provide insights into the evolution of genomic structure, or identify mechanisms that could account for genomic instability in human malignancies. The possibility that G-richness can contribute to shared regulation suggests that genes with similar or related functions may share features of genomic structure. We therefore set out to determine the prevalence of G-rich sequences capable of forming G4 DNA among human genes, and to determine if particular functional classes of genes might be characterized by the presence or absence of G-rich regions.
METHODS
G4P Calculator software
We developed a software program, ‘G4P Calculator’, which computes G4 DNA potential based on the density of runs of guanines in a sequence. The program evaluates runs of guanines in a sliding window and calculates the percentage of windows searched that meet the specified criteria. The criteria used are as follows: G-run length, 3; number of G-runs per window, 4; window length, 100 nt; and sliding interval length, 20 nt. The requirement for four or more runs of three or more guanines is based on studies of oligonucleotide folding . The 100 nt window size facilitates rapid analysis and is easily reduced (or enlarged) for rescan of specific genes of interest. The 20 nt sliding interval length is set such that sequences with the potential to form more than a single G4 DNA structure make correspondingly higher contributions to the total G4P. The last four windows analyzed are processed as windows of progressively smaller size (80, 60, 40 and 20 nt), and this does not affect the results as gene length is much larger than window size. Each DNA strand is evaluated independently. G4P is scored as a percentage, making it independent of sequence length. These criteria are similar to those used by others (4,5), and although not absolute, provide a means to compare G4P between different sequences. The software is written in C# to run on the Microsoft Windows XP operating system. The program and instructions are available on our laboratory website (http://depts.washington.edu/maizels9/). The source code is available upon request.
Sequence and GO data
Sequence data for the human RefSeq genes (NCBI 35 assembly) and associated GO terms were downloaded from the Ensembl database v.32 using BioMart (47) on 4/9/05. Additional Gene Ontology (GO) data were obtained from the GO website (www.geneontology.org) on 3/5/06. Flanking sequence data were downloaded from Ensembl v.34 on 14/10/05. The cDNA sequences were downloaded from the Ensembl v.37 on 19/3/06. For genes with multiple transcript variants, the first listed variant for each gene was evaluated to assess G4P of the cDNA. The median G4 DNA potential was calculated from 16 654 RefSeq genes. Since each gene may have several GO classifications, a total of 77 968 GO term assignments were sorted above or below the median corresponding to 50.5% or 49.5% of the total. The 4524 RefSeq genes that have no GO classification (27% of the total) are equally distributed below and above the median.
Flanking sequence analysis
Analysis of G4P included only genes for which both gene and flanking sequence were complete. Excluded from that analysis were 101 genes for which sequence determination was incomplete (more than four unidentified consecutive bases). The excluded genes are identified in Supplementary Table 1; none of the excluded genes was a known proto-oncogene or a tumor suppressor gene.
Statistical analysis
The Wilcoxon rank sum test was applied by using the statistics program R 2.2.1 (Wilcoxon test parameters: alternative = ‘two-sided’, paired = FALSE). The linear regression, single-factor ANOVA, and standard error analyses were performed using Microsoft Office Excel 2003. Owing to the skewed distribution of G4P, data were subjected to a natural log transformation before linear regression analysis. Genes with G4P = 0 were therefore not included. This resulted in the exclusion of 1152 genes from the correlation between DNA strands (Figure 1B); and of 905 genes from the analysis of G4P versus GC content (Figure 3A).
Figure 1 Potential for G4 DNA formation of human genes. (A) Distribution of genes across G4 DNA formation potential (G4P). The distribution of 16 654 RefSeq genes is illustrated by vertical bars (gray). Median G4P for the RefSeq genes at 5.0% is indicated by a dotted line. The distribution of the 4396 GO terms assigned to 73% of the RefSeq genes is outlined (black). (B) Positive correlation of G4P of template and nontemplate DNA strands. Linear regression analysis of G4P of the nontemplate (y-axis) and the template (x-axis) strand. Owing to the skewed distribution of G4P, the data were subjected to a natural log transformation before linear regression analysis; therefore, a small number of genes with G4P equal to zero were not included. The slope determined by linear regression analysis (0.83) is represented by the solid line; and slope of unity by the dotted line.
CpG islands
The NewCpGReport software (48) was accessed and run from the website http://csc-fserve.hh.med.ic.ac.uk/emboss/newcpgreport.html. The tumor suppressor and proto-oncogene sequences were processed using the program's default settings: window size = 100; shift increment = 1; minimum length = 200; minimum observed/expected = 0.6; and minimum percent = 50.
RESULTS
G4 DNA formation potential of human genes
To score potential for G4 DNA formation, we developed software that analyzes overlapping windows of sequence, and scores each window that contains four or more runs of three or more guanines as a ‘hit’, then quantifies G4 DNA formation potential (G4P) as the percentage of hits in the total number of windows searched. We used this program, ‘G4P Calculator’, to evaluate the G4P of the entire transcribed sequence (exons and introns) of 16 654 human Reference Sequence (RefSeq) genes. Nontemplate and template strands were analyzed separately, to distinguish contributions of transcription-induced structure formation, which affects only the nontemplate strand; and replication-induced structure formation, which affects both strands. G4P of the nontemplate strand ranged from 0 to 79%, with a median of 5.0% and an average of 9.1% (Figure 1A and Supplementary Table 1). Linear regression analysis (Figure 1B) showed that G4P of the nontemplate and template strands is positively correlated (R2 = 0.73), with a slope of less than unity (0.83). Thus, for most genes, there is a slightly lower potential for the formation of transcription-induced structures than replication-induced structures; although potential for the formation of structures on either strand is closely correlated.
Gene function corresponds with G4P
The skewed distribution of G4P over the RefSeq genes (Figure 1A) suggests that most genes cannot readily form G4 DNA structures, but that some genes may be highly susceptible. To identify functional classes of genes with high and low potential for G4 DNA formation, we evaluated the distribution of terms defined by the GO Consortium (49) for each RefSeq gene across the spectrum of G4 DNA potentials. In this classification scheme, 27% of genes currently have no GO terms assigned; and others have been assigned multiple GO terms and are represented in several different categories. The distribution of the 4396 GO terms assigned to the human RefSeq genes across G4 DNA potential proved to be nearly identical to the distribution of genes (Figure 1A).
Restricting further analysis to the 218 GO terms associated with 50 or more genes, 61 GO terms were identified for which 60% or more of the genes were below or above the RefSeq median G4P (24 low G4P and 37 high G4P); and application of the Wilcoxon rank sum test confirmed that these criteria were robust (Table 1 and Supplementary Table 2). Functions characterized by low G4P include G-protein-coupled receptors, sensory perception (especially olfaction), nucleosome assembly, nucleic acid binding, ubiquitin cycle, cell adhesion and cell division; whereas functions characterized by high G4P include transcription factor activity, development, cell signaling, muscle contraction, growth factors and cytokines. Figure 2 represents, for a subset of the GO terms identified with very low and very high G4P, the median and range of G4P relative to all RefSeq genes and all GO terms. In each case, the difference in distribution relative to the RefSeq genes was highly significant (Figure 2). This establishes a relationship between specific gene functions and potential for G4 DNA formation.
Table 1 Gene Ontology (GO) terms with low and high G4P
Figure 2 G4P correlates with gene function. Ranges of G4P for all RefSeq genes (top line) compared with five GO terms overrepresented in low or high G4P. Boxes represent the percentage of genes in each GO category characterized by G4P in the range 0–1.25, 1.25–2.5, 2.5–5.0, 5–10, 10–20% and >20% (colors as indicated). P-values shown on the right represent significance of the difference in distribution between each GO term and the RefSeq genes, as calculated by the Wilcoxon rank sum test.
Tumor suppressor genes are characterized by low G4P and proto-oncogenes by high G4P
Some of the gene functions characterized by low and high G4P (Figure 2 and Table 1) are associated with tumor suppressor genes and proto-oncogenes, respectively. This led us to interrogate the distribution of G4P with respect to genes in these two categories. A list of 55 tumor suppressor genes and 95 proto-oncogenes was compiled (Supplementary Table 3), using the Online Mendelian Inheritance in Man (OMIM) database as a primary source and confirming gene classification by search of the published literature. Comparison of G4P for tumor suppressor genes and proto-oncogenes established a clear and highly significant difference in the range of G4P observed (Wilcoxon rank sum test, P = 10–8; Figure 3A), and in the distribution of G4P for genes in these two categories relative to the 16 654 genes in the RefSeq database (Figure 3B). The distribution of tumor suppressor genes was shifted from the RefSeq median of 5.0% towards low G4 DNA potential with a median of 2.4% (Wilcoxon rank sum test, P = 4 x 10–5); and the distribution of proto-oncogenes was shifted towards high G4P with a median of 11.0% (Wilcoxon rank sum test, P = 7 x 10–5).
Figure 3 Contrasting G4P of tumor suppressor genes and proto-oncogenes. (A) Ranges of G4P for 55 tumor suppressor genes, 95 proto-oncogenes and all 16 654 RefSeq genes. Boxes represent the percentage of genes in each category characterized by G4P in the range 0–1.25, 1.25–2.5, 2.5–5.0, 5–10, 10–20% and >20% (colors as indicated). P-value represents significance of the difference in distribution between the tumor suppressor genes and proto-oncogenes, as calculated by the Wilcoxon rank sum test. (B) Distribution of tumor suppressor genes and proto-oncogenes across G4P. Bars represent the G4P distribution of 55 tumor suppressor genes (blue) and 95 proto-oncogenes (red). The black outline diagrams distribution of all 16 654 RefSeq genes (as in Figure 1A). P-values represent significance of the difference in distribution between each group of genes and the RefSeq genes, as calculated by the Wilcoxon rank sum test.
Table 2 shows the top 10 genes in each category, ranked according to G4P, using a high stringency 40 nt search window. Analysis of G4P using a 40 nt rather than a 100 nt search window decreased the numerical value of G4P for each individual gene, as expected (Table 2 and Supplementary Table 3), but did not affect the relative differences in distribution of the potentials, and further supported the significance of the difference between tumor suppressor genes and proto-oncogenes (Wilcoxon rank sum test, P = 2 x 10–7). Table 2 also shows representative GO terms assigned to each gene, many of which correspond to GO terms overrepresented in low or high G4P (Figure 2).
Table 2 Tumor suppressor genes with low G4P and proto-oncogenes with high G4P
Simulations were carried out to verify the significance of the differences between G4P of tumor suppressor genes and proto-oncogenes. G4P distributions of 95 or 55 genes picked at random were not significantly different from the RefSeq set (P > 0.1), in each of 20 iterations. Furthermore, statistical significance of observed differences was robust to misclassification of up to 10 genes per category, as tested by the addition of 10 randomly selected RefSeq genes to either category (P < 0.002), or by elimination of 10 randomly selected genes from either category (P < 0.0002). These simulations confirmed the significance of the differences between G4P of tumor suppressor genes and proto-oncogenes.
Tumor suppressor genes and proto-oncogenes have similar numbers of CpG islands
CpG islands are associated with a majority of promoters of human genes (50), and CpG dinucleotides are targets for methylation leading to gene silencing . G4P is positively correlated with GC-content (Figure 4A), which could in principle reflect a local enrichment of CpG methylation sites. We tested this possibility by analyzing tumor suppressor genes and proto-oncogenes with the EMBOSS program ‘NewCpGReport’ (48), which identifies CpG-rich regions. The number of CpG-rich regions do not differ significantly between these two categories of genes (Wilcoxon rank sum test, P = 0.4; Figure 4B). Thus, the density of potential methylation sites does not distinguish tumor suppressor genes from proto-oncogenes, or account for the differences in G4P we have documented.
Figure 4 G4P correlates with GC-content but not CpG islands. (A) Correlation of G4P with GC-content. Linear regression analysis of G4P relative to total GC-content (left); the portion of GC-content contributed from G-runs or C-runs (center); the remaining GC-content contributed from Gs and Cs outside of G-runs or C-runs (right). The data were subjected to a natural log transformation before linear regression analysis; therefore, a small number of genes with G4P equal to zero were not included. The slopes determined by linear regression analysis are represented by solid lines. G4P correlates most closely with Gs and Cs within runs (middle). (B) Distribution of tumor suppressor genes and proto-oncogenes relative to number of CpG islands. Closed bars, tumor suppressor genes; open bars, proto-oncogenes. P-value was determined by the Wilcoxon rank sum test comparing tumor suppressor genes to proto-oncogenes, and shows that there is not a significant relationship between gene function and number of CpG islands.
Both exon and intron sequences contribute to G4P
In human genes, the ratio of exon length to intron length is typically well below unity (52), so the measurement of G4P for an entire gene will largely reflect the contribution of intronic sequences. To distinguish contributions of exons and introns to G4P, we analyzed the G4P of one representative cDNA for each tumor suppressor gene and proto-oncogene (Figure 5 and Supplementary Table 3). The median G4P of cDNA sequences is 1.9% for tumor suppressor genes, and 7.6% for proto-oncogenes (Figure 5), in each case slightly lower than G4P for the entire gene (2.4 and 11%, respectively; Figure 3B). The difference between G4P for cDNAs in the two functional categories is highly significant (Wilcoxon rank sum test, P = 6 x 10–6). Thus, both exon and intron sequences contribute to the significant differences in G4P characteristic of tumor suppressor and proto-oncogenes.
Figure 5 Differences in G4P of tumor suppressor and proto-oncogene cDNAs. Distribution of tumor suppressor gene and proto-oncogene cDNA sequences across G4P. Bars represent tumor suppressor genes (closed bars) and proto-oncogenes (open bars). P-value was determined from the Wilcoxon rank sum test comparing tumor suppressor genes to proto-oncogenes.
The G4P of genes contrasts with their genomic environment
The human genome consists of large segments of fairly homogeneous GC-content, defined as isochores (53). With the availability of the human genome sequence, this definition has been honed further, and 100 kb segments of DNA sequence can be sorted into five isochore families with an average SD of 1% GC (54). Since G4P is positively correlated with GC-content (Figure 4A), we asked if G4P for each gene reflects its local genomic environment. To do this, we computed the difference in G4P for each of the RefSeq genes and its flanking sequences, G4P = G4P – G4PFLANK, calculating G4PFLANK as the average G4P for 20 kb upstream and downstream of each gene (Supplementary Table 1 and Figure 6). The average G4P for all RefSeq genes is 1.6%; thus on average, genes have greater G4P than their flanking sequences. Comparison of G4P of the RefSeq genes, the proto-oncogenes and the tumor suppressor genes by a single-factor ANOVA showed that the three groups are distinct (ANOVA, P = 10–5; Figure 6). The average G4P for the set of tumor suppressor genes is –2.2%, much lower than that of the RefSeq genes (ANOVA, P = 5 x 10–5). In contrast, the average G4P for the set of proto-oncogenes is 3.4%, higher than that of the RefSeq genes (ANOVA, P = 0.01), and considerably higher than that of the tumor suppressor genes (ANOVA, P = 5 x 10–7). Thus, on average, tumor suppressor genes have lower G4P than their flanking sequences, and proto-oncogenes have higher G4P than their flanking sequences. Potential for G4 DNA formation therefore correlates with gene function, rather than local genomic environment, for both tumor suppressor genes and proto-oncogenes.
Figure 6 G4P of genes differs from G4P of genomic environment. Average G4P for genes; 20 kb flanking sequences (G4PFLANK); and G4P, the difference between G4P for each gene and its flank. Gray bars, RefSeq genes; closed bars, tumor suppressor genes; and open bars, proto-oncogenes. Standard errors were determined by ANOVA for each analysis of the three groups of genes.
The prototypical tumor suppressor and proto-oncogenes, TP53 and MYC , illustrate the relationship between G4P of genes and their flanking sequences. For TP53, G4P is 7.6%, slightly higher than the RefSeq median of 5%; and G4PFLANK is 12.1% (10.0% upstream and 14.1% downstream). Thus, G4P of TP53 is –4.4%, low even among tumor suppressor genes. In contrast, for MYC, G4P is 18.6%, well above the RefSeq median; and G4PFLANK is 2.8% (3.3% upstream and 2.2% downstream). Thus, G4P of MYC is 15.9%, considerably above the average RefSeq G4P of 1.6%.
DISCUSSION
We have investigated the relationship between potential to form G4 DNA and gene function for the 16 654 human RefSeq genes. We find that there is a highly skewed distribution of G4P among human genes, and that there are robust correlations between G4P and gene function. Interrogation of the subset of 218 GO terms assigned to 50 or more genes showed that low G4P corresponds with functions including G-protein-coupled receptors, olfaction, nucleosome assembly, nucleic acid binding, ubiquitin cycle, cell adhesion and cell division; and high G4P with functions including transcription factor activity, development, cell signaling, growth factors and cytokines. These findings motivated interrogation of two contrasting gene categories defined by the OMIM database, tumor suppressor genes and proto-oncogenes, which showed that genes in these categories are distinguished by low and high G4P, respectively (Figure 3).
In contrast to the robust relationship between G4P and gene function, G4P did not correspond to any of several well-established parameters used to characterize genomic structure. G4P does correlate with GC-content, but not with the number of CpG islands (Figure 4). Both exons and introns contribute to the difference in G4P between tumor suppressor genes and proto-oncogenes (Figure 5). Furthermore, G4P does not reflect the local genomic environment (Figure 6). In fact, tumor suppressor genes have much lower G4P than would be predicted by their genomic environment as compared to the RefSeq genes, whereas proto-oncogenes have higher G4P than would be predicted. The most straightforward interpretation of these results is that genes with specific functions have undergone selection based on G4P.
One source of selective pressure that could contribute to determining G4P is suggested by the association between G-rich regions and genomic instability. Transcription-induced or replication-induced DNA structures can form within regions of high G4P, and if these structures are not faithfully resolved, the result may be genomic instability and impaired gene function. In this view, the low G4P of tumor suppressor genes could reflect evolution that minimized potential instability of genes which function to maintain genomic stability. There is considerable evidence for haploinsufficiency of tumor suppressor genes , and this would contribute to pressure to minimize genomic instability. Conversely, the high G4P that characterizes the proto-oncogenes would be predicted to contribute to their destabilization. Could instability provide a selective advantage? Under some circumstances, it may. Proto-oncogenes are transcribed in rapidly dividing cells and tissues. Transcription-induced structures have considerable potential to contribute to genomic instability (25,26), but they can form only within genes, which represent a relatively small fraction of genomic DNA. The high G4P of the proto-oncogenes would make them targets for transcription-induced destabilization. Proto-oncogenes encode key factors that promote cell proliferation and development, and impaired expression of a proto-oncogene could in turn diminish or prevent cell proliferation, either by decreasing expression of an essential factor, or signaling cell death via apoptosis. Proto-oncogenes may therefore carry out a passive surveillance function, monitoring instability that specifically affected the transcribed fraction of the genome. This surveillance function would necessarily be vested in genes, rather than in the vast landscape of nontranscribed sequences, consistent with the clear differences between G4P of genes and their flanking sequences.
Another mechanism that may contribute to selection based on G4P is shared regulation. Sequences within promoter regions of several proto-oncogenes have been shown to form G4 DNA in vitro (29–32), and factors that bind G4 DNA have been implicated in both transcriptional and translational regulation (28,57). However, regulatory factors typically exert their effects within limited genomic regions, so commonality of short cis-regulatory elements is unlikely to provide a complete explanation for a feature of sequence composition that distinguishes both exons and introns, and extends throughout a gene (Figure 5). Similarly, G4P is unlikely to reflect selection for coding capacity, as this sort of selection would affect exons alone. Nonetheless, there does appear to be some selection against regions of high G4P within exons, as in both gene categories, the median G4P of exons was lower than for introns: 1.9% versus 2.4% for tumor suppressor genes; and 7.6% versus 11% for proto-oncogenes (Figures 3B and 5). Thus high G4P may be disfavored in mature RNAs, as has been proposed previously (4); or incompatible with efficient translation or effective coding.
Several lines of evidence suggest that GC-content may broadly correlate with gene expression levels (58–60); in particular, GC-richness correlates with open chromatin structure, which may in turn facilitate transcription (61). Proto-oncogenes are rapidly transcribed during early development and in response to cell activation, and the high G4P of the proto-oncogenes might reflect GC-richness that contributes to high transcription levels of genes in this group. The finding that potential for G4 DNA formation correlates robustly with specific gene functions suggests that G4P may be a useful parameter to include in global analyses of gene expression, regulation and interactions. Systems-based analyses of this sort should establish whether regulation could contribute to selection based on G4P.
SUPPLEMENTARY DATA
Supplementary Data are available at NAR Online.
ACKNOWLEDGEMENTS
We thank Audrey Qiuyan Fu and Paul Sampson for assistance with statistical analysis; John Newman for independent testing of the G4P Calculator software; and Evan Eichler for comments on the manuscript. This work was supported by NIH R01 GM65988 to N.M. and J.E. was supported by T32 CA009537 . Funding to pay the Open Access publication charges for this article was provided by NIH RO1 GM65988.
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